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Supporting Emergence: Interaction Design for Visual Analytics Approach to ESDA

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Supporting Emergence: Interaction Design for Visual Analytics Approach to ESDA

  1. 1. NSF  Workshop  on    From  OpenSHAPA  to    Open  Data  Sharing  Arlington,  VA,  15-­‐16  Sep  2011   Suppor&ng  Emergence:    Interac&on  Design  for  Visual  Analy&cs   Approach  to  ESDA   William  Wong   Head,  Interac&on  Design  Center   Middlesex  University   London,  UK   15  September  2011     1  
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  3. 3. What  we  do  in  ESDA  •  Tool  usage  in  observa&on,  data  analysis  and  interpreta&on   –  The  resolu,on  (wing  touch),  tool  differences  and  hence  what  can  be  done,  in   different  contexts  eg  development,  learning,  teaching  etc  •  Sharing  of  collected  data   –  Why  would  I  want  to  share   –  If  I  could  share,  what  problems  and  hinderances  •  Very  insighMul  of  the  specific  challenges  and  nuances  of  use  in  each   domain  of  use  •  What  can  we  learn  from  a  different  form  of  “ESDA”  for  a  future   OpenSHAPA  /  OpenSHARE?   –  From  security  and  library  domains   –  Data  sharing  –  ‘common  source’  but  used  by  different  analysts   –  While  analysis  is  crucial,  sense-­‐making  to  draw  conclusions  based  on  assembled   evidence  for  making  decisions  is  paramount   –  Use  Interac,ve  Visualisa,on  to  couple  intelligent  analysis  (e.g.  automa,c  en,ty   extrac,on,  automa,c  thema,c  analysis)  with  emergence  driven  user  interface   design   3  
  4. 4. Learning  from  a  Security  and  Library   Perspec&ve  •  Making  Sense   –  EPSRC,  9  UK  Universi,es;  Imperial  College  PI,  MU  Deputy  PI   –   Mul,-­‐disciplinary  approach,  as  the  problem  cannot  be  addressed  by  a  single   discipline  e.g.  image  analysis,  corpus  linguis,cs  and  automated  en,ty  extrac,on,   soVware  forensics,  systems  engineering,  representa,on  design,  psychology,  law    •  UKVAC  Phase  2   –  US  DHS  and  UK  HM  Government,  5  UK  Universi,es,  Coordinator  MU   –  Mul,-­‐disciplinary  approach  to  Nobel  Laureate  and  FAA  Flight  Data  Problem  •  INVISQUE   –  JISC  Rapid  Innova,on  Programme,  MU  PI   –  Conceived  as  next  genera,on  alterna,ve  to  difficult-­‐to-­‐use  library  e-­‐resources  =>   tangible  reasoning  workspace   –  Taylor  and  Francis    •  Visual  Analy&cs  BLWWong©2011   4  
  5. 5. What  is  Visual  Analy&cs?  •  Visual  analy&cs  is  the  science  of  analy&cal  reasoning  facilitated  by   interac&ve  visual  interfaces  (Thomas  and  Cook,  2005).   –  Integra,ng  tools  for  interac,ng    with  the  abstract  human  thinking  and   reasoning  processes   –  Manipula,on  helps  in  reasoning  by  enabling  the  user  to  re-­‐arrange  the   problem  space  (Maglio  et  al,  1999)  •  Data  graphics  or  info  vis  are  sta&c  •  VA  combines  interac5ve  visualiza5ons  based  on  analy5c  tools  to   enable  rapid  querying  and  interroga&on  of  informa&on  …   –  Visual  form  includes  charts,  network  graphs,  rela,onships  over  ,me  and/ or  (geographical)  space   –  enables  explora,on  through  rapid  and  repeated  querying   –  access  to  original  data,   –  analysis  of  data     –  genera,on  of  hypotheses     –  construc,on  of  conclusion  pathways  •  …  for  the  purpose  of  sense-­‐making   –  The  ability  to  rapidly  (and  visually)  process  and  assemble  evidence  to   enable  genera,on  of  explana,ons  or  conclusions,  enabling  decisions   5  
  6. 6. The  Visual  Analy&cs  Problem:  Emergence,   Search  and  Explana&on   Visually  supported  analy,c  reasoning   Varied  media,  varied  analysis  and  presenta,on  tools   Frame  of   Reference   Lack  of  the  ‘big  picture’   Keyhole   problem   Jig-­‐saw  puzzle   (not  one,  but  many)  Large  data  sets:  mul,-­‐sourced,  mixed-­‐format,  silo-­‐based,  sta,c/stream,  out  of  sequence,  uncertain  and  varying  quality   BLWWong©2011  
  7. 7. 20  Representa&on  and  Analy&c  Problems  1.  The  problem  of  seeing  a  large  data  set  and  reasoning  space  through  a  small  keyhole.  2.  The  problem  of  handling  missing  data.  3.  The  problem  of  handling  decep&ve  /  misleading  data.  4.  The  problem  of  handling  contradictory  data.  5.  The  problem  of  aggrega&ng  and  reconciling  mul&ple  points  of  view  or  predic&ons.  6.  The  problem  of  evidence  colla&on  and  eviden&al  reasoning.  7.  The  problem  of  provenance  and  tracing  analy&c  reasoning.  8.  The  problem  of  integra&ng  data  space,  analy&c  space  and  hypothesis  spaces.  9.  The  problem  of  handling  strength  of  evidence  (including  subjec&ve  and  objec&ve  measures  of  strength)  +   contribu&on  of  different  pieces  of  evidence  to  a  conclusion.  10.  The  problem  of  handling  uncertainty  in  data  and  /  or  informa&on.  11.  The  problem  of  represen&ng  and  handling  evidence  over  &me  and  space.  12.  The  problem  of  annota&ng,  remembering,  re-­‐visi&ng,  and  sehng  aside.  13.  The  problem  of  developing  a  sense  of  what  is  in  the  data  –  exploring  what  is  there.  14.  The  problem  of  predic&ng  and  represen&ng  emergent  behaviour.  15.  The  problem  of  Iden&fying  and  represen&ng  trends.  16.  The  problem  of  recognising  and  represen&ng  anomalous  data.  17.  The  problem  of  finding  the  needle  in  the  haystack  (or  knowing  what  is  chaff  –  i.e.  info  of  no  or  low  value)  18.  The  problem  of  predic&ng  the  path  of  cascading  failures  or  effects.  19.  The  problem  or  represen&ng  the  sta&c  and  dynamic  rela&onship  between  the  data  /  informa&on.  20.  The  problem  of  scalability  and  reusability.     7  BLWWong©2011    
  8. 8. Visual  Analy&cs  Concept   Interac,ve  Dynamic  querying   Visualiza,on  of  Output   Palerns  and  commonali,es   Filters   Seman,c  Extrac,on   Data  integra,on   &  transforma,on   Many  tools   Sensors  /  Surveillance  /  Data  collec,on   “SoV”  Data   “Hard”  Data   Social  networks,  interac,ons,  ac,vi,es  BLWWong(c)2010   8  
  9. 9. Architecture:  Many  Tools  
  10. 10. Indexing,  Structuring  and  Theorizing:   Visual  Analy&cs  and  OpenSHAPA     Indexing   Structuring   Theorizing  Data  Sets   Automated   Schema,za,on   Explana,ons  -­‐  Structured   en,ty  extrac,on   Search  and  query   Hypothesis   and   Analy,cal  tools   tes,ng   unstructured   Colla,on   for  topical,   Eviden,al   text   geospa,al,   Thema,c  analysis  -­‐  Video   reasoning   temporal,  -­‐  Speech   Conclusion   network  analysis  Not  just  reports   pathways  and  video,  but  also  social  media   Provenance  –  data,  processes,  and  reasoning:  Traceability,  how  did  we  get  here?   10  
  11. 11. Emergent  Themes  Analysis   Representation Broad Themes Design Concepts Related excerpts from transcripts e.g. Goals Decision StrategiesTranscripts Interpret & Conceptualise e.g. Planning Specific themes Excerpts relating to specific Narratives concepts in a theme, e.g. types of activities, examples of cues e.g. Assessment of Resources Activities Cues Knowledge Difficulties Identify, Index & e.g. Assessment Collate e.g. Control e.g. Assessment of Structuring & Situation Data reduction  Wong©2004   11  
  12. 12. INVISQUE  demo:  Interac&on  Design  for   Suppor&ng  Emergence  •  INterac&ve  VIsual  Search  and  QUery  Environment   –  Visual  forms  alempt  to  create  palerns  that  reinforce   relaIonships  (CSE)   –  Interac,on  designed  to  support  emergence  in  themaIc   analysis  •  INVISQUE  JISC  Library  Version   –  Suppor,ng  sense-­‐making  –  Data-­‐Frame  Model   –  Using  the  basic  interac,ve  visualiza,on  techniques   developed  here  to  support  sense-­‐making  in  inves,ga,ve   domains   12  
  13. 13. The  Interac&ve  Visualiza&on  Approach  •  Informa&on  Design  Principles   –  Focus+Context   –  Proximity-­‐Compa,bility  Principle   –  Gestalt  Principles  of  Form  Percep,on   –  Principle  of  Visual  Affordances   –  Ecological  Interface  Design   –  Representa,on  of  Func,onal  Rela,onships     13  
  14. 14. The  Interac&ve  Visualiza&on  Approach  •  Principles  implemented  in  design  by   –  Anima,on,  transparency,  informa,on  layering,  spa,al   layout,  palern  crea,on   –  Emphasizing  the  representa,on  of  rela,onships  within  the   data   –  Discovery  of  expected  and  un-­‐an,cipated  rela,onships   –  Interac,on  techniques  enable  rapid  and  con,nuous   itera,ve  querying  and  searching  while  keeping  visible  the   context  of  search   –  Minimizing  WWILF-­‐ing,  or  the  ‘What  Was  I  Looking  For?’   problem   14  
  15. 15. The  Data-­‐Frame  Model    Guides  Interac&on  Design     Klein  et  al,  2007   15  
  16. 16. Reasoning  workspace  framework:   Mapping  and  design  and  of  reasoning  work  to  the  “keyhole”     Hypothesis  Space   Depic,on  of   -­‐ Collate,  assemble,  marshal   “reasoning  and   -­‐ Formula,on     search  process”   -­‐ Tes,ng  and  simula,on   -­‐ arguments,  conclusions,   “brushing”   evidence  Depic,on  of  “Data  terrain”   Conclusion  Pathways   Data  Space   Analysis  Space   -­‐ Tools  and  algorithms   -­‐ what’s  available?     -­‐ Behaviours,  rela,onships   -­‐ What’s  changed?   and  palerns   -­‐ Awareness:  what’s  in  there?   -­‐ what’s  going  on  in  there?   Transla&on  into  Design   BLWWong(c)2010   16  
  17. 17. Conclusion:  Some  Ques&ons  •  What  can  we  do  for  a  future  OpenSHAPA  and  OpenSHARE?   –  indexing,  structuring,  bearing  in  mind  future  will  have  lots  of  “smart”   analysis  technologies  that  can  support  the  lower  levels  of  analysis,   par,cularly  indexing  •  What  System  Architecture?   –  that  combines  data  from  different  sources,  and  allows  a  variety  of   tools  to  analyse  and  make  sense  of  data  •  Alterna&ve  designs  for  structuring  and  theorizing  that  more   directly  support  sense-­‐making?   –  Adopt  /  adapt  an  interac,ve  visualisa,on  interface  design   –  Focus  on  emergence,  search  and  sense-­‐making   •  Emergence  techniques  such  as  “Temporal  narra,ves”   •  Mul,ple  threads  /  parallel  lines  of  enquiry  and  finding  intersec,ng  storylines   –  Reasoning  workspace  for  assembling  our  thoughts  and  conclusions  •  Future  work:  Collabora&ve  Sense-­‐making  environments   17  
  18. 18. End   18  

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